:py:mod:`bluecast.conformal_prediction.effectiveness_nonconformity_measures`
============================================================================

.. py:module:: bluecast.conformal_prediction.effectiveness_nonconformity_measures


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   bluecast.conformal_prediction.effectiveness_nonconformity_measures.convert_expected_effectiveness_nonconformity_input_types
   bluecast.conformal_prediction.effectiveness_nonconformity_measures.one_c
   bluecast.conformal_prediction.effectiveness_nonconformity_measures.avg_c
   bluecast.conformal_prediction.effectiveness_nonconformity_measures.prediction_interval_spans



.. py:function:: convert_expected_effectiveness_nonconformity_input_types(y_hat: Union[numpy.ndarray, pandas.Series, pandas.DataFrame]) -> numpy.ndarray


.. py:function:: one_c(y_hat: Union[numpy.ndarray, pandas.DataFrame, pandas.Series])

   Calculate proportion of singleton sets among all prediction sets.
   :param y_hat: Predicted probabilities of shape (n_samples, 1) where each row is a set of classes.


.. py:function:: avg_c(y_hat: Union[numpy.ndarray, pandas.DataFrame, pandas.Series]) -> float

   Calculate the average number of labels in all prediction sets.
   :param y_hat: Predicted probabilities of shape (n_samples, 1) where each row is a set of classes.


.. py:function:: prediction_interval_spans(prediction_intervals: pandas.DataFrame, alphas) -> Dict[float, float]

   Calculate the mean span or width prediction intervals.

   This checks the distance between low and high band for each alpha.
   :param prediction_intervals: Predicted bands according to provided confidence levels.
   :param alphas: List of alphas indicating which confidence levels to check